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[ICLR 2020] Learning Compositional Koopman Operators for Model-Based Control

[ICLR 2020] Learning Compositional Koopman Operators for Model-Based Control

[ICLR-21 simDL] [Invited Talk] Compositional Dynamics Modeling for Physical Inference and Control

[ICLR-21 simDL] [Invited Talk] Compositional Dynamics Modeling for Physical Inference and Control

Mario Sznaier - A Convex Optimization Approach to Learning Koopman Operators from Data

Mario Sznaier - A Convex Optimization Approach to Learning Koopman Operators from Data

Derivative-Based Koopman Operators for Real-Time Control of Robotic Systems

Derivative-Based Koopman Operators for Real-Time Control of Robotic Systems

Yunzhu Li - Learning-based Dynamics Modeling for Physical Inference and Model-based Control

Yunzhu Li - Learning-based Dynamics Modeling for Physical Inference and Model-based Control

“Deep Koopman” demos

“Deep Koopman” demos

SIAM DS21: Igor Mezić - Koopman Operator, Geometry, and Learning of Dynamical Systems

SIAM DS21: Igor Mezić - Koopman Operator, Geometry, and Learning of Dynamical Systems

Local Koopman Operators for Data-Driven Control of Robotic Systems

Local Koopman Operators for Data-Driven Control of Robotic Systems

Koopman-operator-based Attitude Dynamics and Control on SO(3)

Koopman-operator-based Attitude Dynamics and Control on SO(3)

Autonomous Parking with data-driven control-Deep Koopman Representation

Autonomous Parking with data-driven control-Deep Koopman Representation

Koopman Operator Theory Based Machine Learning of Dynamical Systems, Igor Mezic

Koopman Operator Theory Based Machine Learning of Dynamical Systems, Igor Mezic

ModulOM presentation for Rethinking ML Papers workshop - ICLR 2021

ModulOM presentation for Rethinking ML Papers workshop - ICLR 2021